Learning Deterministic Finite Automata from Infinite Alphabets

Gaetano Pellegrino, Christian Hammerschmidt, Qin Lin, Sicco Verwer
Proceedings of The 13th International Conference on Grammatical Inference, PMLR 57:120-131, 2017.

Abstract

We proposes an algorithm to learn automata infinite alphabets, or at least too large to enumerate. We apply it to define a generic model intended for regression, with transitions constrained by intervals over the alphabet. The algorithm is based on the Red & Blue framework for learning from an input sample. We show two small case studies where the alphabets are respectively the natural and real numbers, and show how nice properties of automata models like interpretability and graphical representation transfer to regression where typical models are hard to interpret.

Cite this Paper

BibTeX
@InProceedings{pmlr-v57-pellegrino16, title = {Learning Deterministic Finite Automata from Infinite Alphabets}, author = {Pellegrino, Gaetano and Hammerschmidt, Christian and Lin, Qin and Verwer, Sicco}, booktitle = {Proceedings of The 13th International Conference on Grammatical Inference}, pages = {120--131}, year = {2017}, editor = {Verwer, Sicco and Zaanen, Menno van and Smetsers, Rick}, volume = {57}, series = {Proceedings of Machine Learning Research}, address = {Delft, The Netherlands}, month = {05--07 Oct}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v57/pellegrino16.pdf}, url = {http://proceedings.mlr.press/v57/pellegrino16.html}, abstract = {We proposes an algorithm to learn automata infinite alphabets, or at least too large to enumerate. We apply it to define a generic model intended for regression, with transitions constrained by intervals over the alphabet. The algorithm is based on the Red & Blue framework for learning from an input sample. We show two small case studies where the alphabets are respectively the natural and real numbers, and show how nice properties of automata models like interpretability and graphical representation transfer to regression where typical models are hard to interpret.} }
Endnote
%0 Conference Paper %T Learning Deterministic Finite Automata from Infinite Alphabets %A Gaetano Pellegrino %A Christian Hammerschmidt %A Qin Lin %A Sicco Verwer %B Proceedings of The 13th International Conference on Grammatical Inference %C Proceedings of Machine Learning Research %D 2017 %E Sicco Verwer %E Menno van Zaanen %E Rick Smetsers %F pmlr-v57-pellegrino16 %I PMLR %P 120--131 %U http://proceedings.mlr.press/v57/pellegrino16.html %V 57 %X We proposes an algorithm to learn automata infinite alphabets, or at least too large to enumerate. We apply it to define a generic model intended for regression, with transitions constrained by intervals over the alphabet. The algorithm is based on the Red & Blue framework for learning from an input sample. We show two small case studies where the alphabets are respectively the natural and real numbers, and show how nice properties of automata models like interpretability and graphical representation transfer to regression where typical models are hard to interpret.
RIS
TY - CPAPER TI - Learning Deterministic Finite Automata from Infinite Alphabets AU - Gaetano Pellegrino AU - Christian Hammerschmidt AU - Qin Lin AU - Sicco Verwer BT - Proceedings of The 13th International Conference on Grammatical Inference DA - 2017/01/16 ED - Sicco Verwer ED - Menno van Zaanen ED - Rick Smetsers ID - pmlr-v57-pellegrino16 PB - PMLR DP - Proceedings of Machine Learning Research VL - 57 SP - 120 EP - 131 L1 - http://proceedings.mlr.press/v57/pellegrino16.pdf UR - http://proceedings.mlr.press/v57/pellegrino16.html AB - We proposes an algorithm to learn automata infinite alphabets, or at least too large to enumerate. We apply it to define a generic model intended for regression, with transitions constrained by intervals over the alphabet. The algorithm is based on the Red & Blue framework for learning from an input sample. We show two small case studies where the alphabets are respectively the natural and real numbers, and show how nice properties of automata models like interpretability and graphical representation transfer to regression where typical models are hard to interpret. ER -
APA
Pellegrino, G., Hammerschmidt, C., Lin, Q. & Verwer, S.. (2017). Learning Deterministic Finite Automata from Infinite Alphabets. Proceedings of The 13th International Conference on Grammatical Inference, in Proceedings of Machine Learning Research 57:120-131 Available from http://proceedings.mlr.press/v57/pellegrino16.html.

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